Career as a data scientist: education and skills you need

Data has been advancing rapidly towards the forefront of enterprises for many years. It is now possible to store massive amounts of data produced by sales, customer interactions, and digital experiences with the availability of advanced storage technologies.

The business world is constantly flooded with massive amounts of data thanks to mechanisms that simplify the integration of different systems.

Data technologies have succeeded in transforming this data into useful information. The future, however, will see more data appear. Massive data repositories present a tremendous opportunity for organizations if used effectively. Decision science can help in this situation.


Integrating data and using it in ways that help stakeholders make important business decisions is a requirement of decision science.

Making sound inferences from data, telling compelling stories, recognizing relevant pain points, and then accurately applying that information to the appropriate set of business problems are all examples of effectively using data to make informed business decisions.

A profession in decision science involves developing answers based on reliable probabilistic, predictive, experimental, and computational principles.

Decision sciences are very important in the modern world. Decision sciences help to improve judgment. Making a decision involves a number of processes including understanding the problem, using data, using tools, and getting information.


A very solid education is usually required to acquire the amount of knowledge required to be a data scientist, although there are notable exceptions. Data scientists are highly skilled; 88% have at least a master’s degree and 46% have a doctorate.

A bachelor’s degree in statistics, computer science, social sciences, or physical sciences could prepare you to work as a data scientist. Computing (19%) and engineering (16%) are the most popular fields of study after mathematics and statistics (32%).

Your ability to process and evaluate big data will be aided by a degree in one of these programs.

Here are five skills you need to become a data scientist:


For data science, R is generally recommended, but proficiency in at least one of these analytical tools is required. Data science needs are uniquely addressed by R.

Any problem you encounter in data science can be solved using R. R is actually used to solve statistical problems by 43% of data scientists. The learning curve for R, however, is steep.


Along with Java, Perl or C/C++, Python is the most popular coding language that I often consider necessary in data science jobs. For data scientists, Python is a wonderful programming language.

Python is the primary programming language used by 40% of respondents to an O’Reilly study.


The majority of data scientists lack a strong foundation in machine learning topics and methods. These include neural networks, adversarial learning, reinforcement learning, etc.

Know machine learning techniques like supervised machine learning, decision trees, logistic regression, etc. will help you stand out from other data scientists.

You can use these capabilities to solve various data science problems based on predictions of important organizational outcomes.

Here are the qualifications and skills you need to develop a career as a data scientist.


Although NoSQL and Hadoop have become important parts of data science, a candidate is still expected to be able to build and execute sophisticated SQL queries.

Using the SQL (Structured Query Language) programming language, you can add, delete, and extract data from databases.

You can use it to perform analytical tasks and modify the database architecture. As a data scientist, you must master SQL. Indeed, SQL was created to allow you to access, communicate and work with data.


When hiring a great data scientist, companies are looking for someone who can communicate their technical findings to a non-technical team, such as marketing or sales, in an efficient and fluid manner.

In addition to knowing the needs of their non-technical colleagues in order to manage data effectively, a data scientist must empower the business to make decisions by arming them with quantitative information.

The Data Scientist plays a crucial role in integrating data elements that have been extracted from small silo-specific pockets and putting them together by applying their understanding of business dynamics, intuition and long-term vision to build the overall picture.

In a nutshell, decision scientists are creatives who blend the various sciences of math, technology, and business to accomplish their tasks.

These abilities are useful in decision science work and help provide accurate solutions. To develop solutions that support decision making, decision scientists examine data related to the business problem.

– Article by Dr Sibaram Khara, Vice Chancellor, University of Sharda

— ENDS —

Sam D. Gomez